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11.发明授权公开(公告)号：US12249119B2
公开(公告)日：2025-03-11
申请号：US17654019
申请日：2022-03-08
Applicant: GE Precision Healthcare LLC
Inventor： Pavan Annangi , Deepa Anand
IPC: G06T7/00 , G06V10/764 , G06V10/774
Abstract: Systems and method for domain adaptation using pseudo-labelling and model certainty quantification for video data are provided. The method includes obtaining a source data and a target data each comprising a plurality of frames for processing by a machine learning module. The method comprises testing the target data to identify if a minimum number of frames exhibit a frame confidence score based on the source data and identifying salient region within the target data and measuring a degree of spatial consistency of the salient region over time. The method comprises identifying class specific attention region within the target data and measuring a confidence score of class specific attention region within the target data and carrying out pseudo-labeling of the target data based on the source data and calculating a certainty metrics value based on the frame confidence score, the degree of spatial consistency of the salient region over time, the confidence score of class specific attention region within the frames of the target data and confidence score of the pseudo-labeling on the target data. The machine learning module is retrained till the certainty metrics value reaches peak and further retraining the machine learning module does not increase the certainty metrics value.
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公开(公告)号：US12249023B2
公开(公告)日：2025-03-11
申请号：US18065964
申请日：2022-12-14
Applicant: GE Precision Healthcare LLC
Inventor： Deepa Anand , Bipul Das , Vanika Singhal , Rakesh Mullick , Sanjay Kumar NT
Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.
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公开(公告)号：US20250045951A1
公开(公告)日：2025-02-06
申请号：US18362224
申请日：2023-07-31
Applicant: GE Precision Healthcare LLC
Inventor： Bipul Das , Deepa Anand , Vanika Singhal , Rakesh Mullick
Abstract: Systems/techniques that facilitate explainable confidence estimation for landmark localization are provided. In various embodiments, a system can access a three-dimensional voxel array captured by a medical imaging scanner and can localize, via execution of a first deep learning neural network, a set of anatomical landmarks depicted in the three-dimensional voxel array. In various aspects, the system can generate a multi-tiered confidence score collection based on the set of anatomical landmarks and based on a training dataset on which the first deep learning neural network was trained. In various instances, the system can, in response to one or more confidence scores from the multi-tiered confidence score collection failing to satisfy a threshold, generate, via execution of a second deep learning neural network, a classification label that indicates an explanatory factor for why the one or more confidence scores failed to satisfy the threshold.
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公开(公告)号：US20240379226A1
公开(公告)日：2024-11-14
申请号：US18313775
申请日：2023-05-08
Applicant: GE Precision Healthcare LLC
Inventor： Pavan Annangi , Deepa Anand
Abstract: Systems/techniques that facilitate data candidate querying via embeddings for deep learning refinement are provided. In various embodiments, a system can access a test data candidate provided by a client, generate, via a first deep learning neural network, an inferencing output based on the test data candidate, and access feedback indicating whether the client accepts or rejects the inferencing output. In various aspects, the system can generate, via at least one second deep learning neural network, at least one embedding based on the test data candidate. In various instances, the system can, in response to the feedback indicating that the client rejects the inferencing output, identify, in a candidate-embedding dataset, one or more data candidates whose embeddings are within a threshold level of similarity to the at least one embedding and can retrain the first deep learning neural network based on the one or more data candidates.
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公开(公告)号：US12048521B2
公开(公告)日：2024-07-30
申请号：US17973855
申请日：2022-10-26
Applicant: GE Precision Healthcare LLC
Inventor： Dattesh Dayanand Shanbhag , Chitresh Bhushan , Deepa Anand , Kavitha Manickam , Dawei Gui , Radhika Madhavan
CPC classification number: A61B5/055 , G01R33/20 , G01R33/5608
Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.
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Patent Agency Ranking
公开(公告)号：US20250104270A1
公开(公告)日：2025-03-27
申请号：US18475406
申请日：2023-09-27
Applicant: GE Precision Healthcare LLC
Inventor： Deepa Anand , Dattesh Dayanand Shanbhag , Chitresh Bhushan , Dawei Gui , Kavitha Manickam , Maggie MeiKei Fung , Gurunath Reddy Madhumani
IPC: G06T7/73 , G06T7/00 , G06V10/25 , G06V10/26 , G06V10/44 , G06V10/762 , G06V10/774 , G06V20/70
Abstract: A method for performing one-shot anatomy localization includes obtaining a medical image of a subject. The method includes receiving a selection of both a template image and a region of interest within the template image, wherein the template image includes one or more anatomical landmarks assigned a respective anatomical label. The method includes inputting both the medical image and the template image into a trained vision transformer model. The method includes outputting from the trained vision transformer model both patch level features and image level features for both the medical image and the template image. The method still further includes interpolating pixel level features from the patch level features for both the medical image and the template image. The method includes utilizing the pixel level features within the region of interest of the template image to locate and label corresponding pixel level features in the medical image.
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公开(公告)号：US20250032086A1
公开(公告)日：2025-01-30
申请号：US18225811
申请日：2023-07-25
Applicant: GE Precision Healthcare LLC
Inventor： Stephan Anzengruber , Balint Czupi , Pavan Kumar V. Annangi , Deepa Anand , Bhushan Patil , Cindy L. Smrt , Martin Swoboda
Abstract: Systems and methods for enhancing visualization and documentation of fibroid quantity, size, and location with respect to a uterus and endometrium in ultrasound imaging are provided. The method includes receiving, by at least one processor, an ultrasound volume including ultrasound image data of a region of interest that includes a uterus. The method includes automatically segmenting, by the at least one processor, the uterus and an endometrium in the ultrasound volume. The method includes segmenting one or more fibroids in the ultrasound image data. The method includes generating a classification of each of the one or more fibroids based on a location of each of the one or more fibroids with respect to the endometrium and the uterus. The method includes causing, by the at least one processor, a display system to present at least one image identifying the segmented uterus, endometrium, and one or more fibroids.
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公开(公告)号：US12211202B2
公开(公告)日：2025-01-28
申请号：US17500366
申请日：2021-10-13
Applicant: GE Precision Healthcare LLC
Inventor： Deepa Anand , Annangi V. Pavan Kumar
IPC: G06T7/00 , G06N3/047 , G06N3/0475 , G06N3/088 , G06N3/0895 , G06N3/096 , G06T7/11 , G06V10/25 , G06V10/74 , G06V10/762 , G06V10/774 , G06V10/82
Abstract: Techniques are described for learning feature representations of medical images using a self-supervised learning paradigm and employing those feature representations for automating downstream tasks such as image retrieval, image classification and other medical image processing tasks. According to an embodiment, computer-implemented method comprises generating alternate view images for respective medical images included in set of training images using one or more image augmentation techniques or one or more image selection techniques tailored based on domain knowledge associated with the respective medical images. The method further comprises training a transformer network to learn reference feature representations for the respective medical images using their alternate view images and a self-supervised training process. The method further comprises storing the reference feature representations in an indexed data structure with information identifying the respective medical images that correspond to the reference feature representations.
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19.发明公开公开(公告)号：US20240285256A1
公开(公告)日：2024-08-29
申请号：US18175307
申请日：2023-02-27
Applicant: GE Precision Healthcare LLC
Inventor： Pavan Annangi , Deepa Anand , Stephan Anzengruber , Bhushan D. Patil , Arathi Sreekumari
CPC classification number: A61B8/483 , A61B8/466 , G06T2207/20084
Abstract: Various methods and ultrasound imaging systems are provided for segmenting an object. In one example, a method includes accessing a volumetric ultrasound dataset, receiving an identification of a seed point for an object in an image generated based on the volumetric ultrasound dataset, and implementing a two-dimensional segmentation model on a first plurality of parallel slices based on the seed point to generate a first plurality of segmented regions. The method includes implementing the two-dimensional segmentation model on a second plurality of parallel slices based on the seed point to generate a second plurality of segmented regions. The method includes generating a detected region by accumulating the first plurality of segmented regions and the second plurality of segmented regions. The method includes implementing a shape completion model to generate a three-dimensional shape model for the object, and displaying rendering of the object based on the three-dimensional shape model.
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公开(公告)号：US20240203039A1
公开(公告)日：2024-06-20
申请号：US18065964
申请日：2022-12-14
Applicant: GE Precision Healthcare LLC
Inventor： Deepa Anand , Bipul Das , Vanika Singhal , Rakesh Mullick , Sanjay Kumar NT
Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.
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